Reduction of false negatives in multi-class sentiment analysis

Chris Aloysius, P. Tamil Selvan


Sentiment analysis classifications are done as positive, negative, as well as neutral ones. The increased usage of social media and its effects on society call for a more thorough, fine-grained explanation than that. In this study, classification is done in five classes-strongly positive, weakly positive, neutral, weakly negative, and strongly negative-in a more precise manner. Instead of using the typical ways of measuring accuracy alone, a novel method to eliminate false negatives (FN) is focused together with a fine-grained categorization. A bigger risk in sentiment analysis is a false negative. FN classification occurs when the context's polarity is identified as True when it is actually false. A complex dataset is used in this research for the experimental study, and the entire dataset is separated into five classes. Each class's FN are assessed using the suggested methodology. Comparing the proposed strategy to other, it was found to achieve about 53% more reduction in FN cases than rule based models and better predictions than compared machine learning models.


Lexicon model; Machine learning; Multiclass analysis; Sentiment analysis; SVM classifier

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).